For 3D segmentation, all the models are trained from scratch due to the lack of open-source pretrained encoders. Downsampling is performed with strided \(3 \times 3 \times 3\)  padded convolutions, and upsampling is implemented with trilinear interpolation. To further improve the performance on BraTS dataset, we adopt region-based training strategy (optimizing directly on the overlap regions rather than independent labels) and enhancing tumor suppression (replacing predicted enhancing tumor with necrosis if the predicted volume of the enhancing tumor is smaller than a certain threshold) in the training process. For the loss function, we adopt the linear combination of Dice Loss[1]} and Cross Entropy Loss as the loss function in the both binary and multi-class phase in the network.
